1. Technical Field
The disclosure relates to fingerprint identification technologies, and in particular to a high-speed fingerprint identification system and method according to triangle classifications.
2. Related Art
Generally Biometrics uses a portion of biological features on a human body to identify a person's actual identity. Commonly biometrics is applied through face identification, iris identification, vein identification and fingerprint identification. Among these biometric identification technologies, currently fingerprint identification is the broadest application in the market and also one of the easiest applications to be realized at a low cost. Since the possibility of two perfect identical human fingerprints are almost next to nothing, fingerprint identification is broadly used on criminal evidence collections from 19 century.
A fingerprint is formed by raised ridges and concave valleys surrounding each other. Combinations of ridges and valleys on a fingerprint may form various fingerprint features, such as “terminations”, “bifurcation”, “lake”, “independent ridge”, “island”, “spur”, “crossover” and etc. These fingerprint features may all be used as the basis of fingerprint identification. Among all the different fingerprint features, “Ridge Termination Point” and “Ridge Bifurcation Point” are the most fundamental “Standard Feature Points”.
The most significant performance factors of a fingerprint identification system are search time, filtering rate and accuracy. Conventionally a fingerprint needs to be compared with multiple fingerprints during a one-to-many fingerprint identification process; namely a scanned unknown fingerprint image is directly processed with characteristic comparisons to every fingerprint feature file of all the filed fingerprints stored in a database. Such method is undoubtedly the simplest yet the most time consuming. When applied to an electrical information device (e.g. notebook computer) as a login security mechanism, since the fingerprint feature files required to be compared are relatively fewer, such direct comparison method may be sufficient to fulfill the requirements. However, when applied to building entry controls or crime investigations, since the compared fingerprint feature files are at least hundreds and thousands or even hundred thousands, the fingerprint identification efficiency of conventional one-to-many method is quite low.
In order to enhance the efficiency, high-speed fingerprint systems nowadays do not use “Standard Feature Points” to conduct direct comparison tasks; instead, additional “specific features” are adopted as well, such as Fingerprint Directions, Core Points, Delta Points and etc. These fingerprint identification systems have to additionally retrieve, identify and classify these specific features, which leading to a much more complex system framework.